Network planning may be described as the optimized selection of alternative paths through a given network to meet estimates of traffic demand on the network and other operational constraints such as delay and reliability. Reliable traffic demand estimation is therefore a critical step in the network planning process. In telephone networks that cater to a single dominant traffic type, namely voice, the traditional process of off-line traffic forecasting, where a forecast is fed into an off-line network planning process, has proven to be effective. Such an approach to traffic demand estimation uses well established voice traffic models to predict traffic demand within acceptable accuracy limits. The growth rate of traffic demand in telephone networks has traditionally allowed a cycle of network measurement, analysis, forecasting, network planning and deployment to occur over weeks, even months.
With the explosive growth of Internet traffic, the use of Internet protocol (IP) networks for real-time services (Voice over IP, video conferencing) and mission-critical applications and the emergence of e-commerce, data traffic volume has surpassed voice traffic volume and is expected to grow at a much higher rate. Network operators have shifted their planning focus to data-centric network infrastructures that can support a wide range of applications, including voice, scale well with the growth of data traffic and adapt quickly to new market needs. The networks resulting from this shift in planning focus are, at the same time, required to be dependable. That is, the resulting networks preferably provide performance, availability and security equivalent to telephone networks. In such a volatile environment, one of the key challenges to network planners is specification, with sufficient confidence, of the characteristics, demand and rate of growth of data traffic. The data traffic may arise from a wide range of known applications as well as data traffic arising from novel applications. Unfortunately, this specification is required in an environment with little or no historical data. Further, business requirements for these new networks include minimization of time to market, network capacity growth faster than growth of customer base and rapid accommodation of factors unknown during pre-deployment planning.
Typical networks comprises nodes and links between the nodes. Further, connections exist from so-called edge nodes within the network of interest to networks external to the network of interest. Often, a network will be called upon to provide a path from one edge node to another edge node, to connect two external networks. Such a path may be set up at an edge node, responsive to a request for service from a node in an external network, and comprise at least one link between nodes in the network of interest. The request for service may include a requirement for a certain Quality of Service (QoS) relating to path characteristics like bandwidth and delay. At the edge node, once a path which satisfies the request is determined, capacity may be reserved in links along that path. A tunnel, as used herein, refers to an edge-to-edge logical path with capacity, reserved on each link in the path, sufficient to provide QoS guarantees.
Existing networking technologies involve such functions as tunnel signaling, path selection and admission control. Network nodes use tunnel signaling to establish tunnels, reserve capacity for tunnels, modify reserved capacity or tear tunnels down. Known network architectures include such examples of tunnels as Virtual Paths in Asynchronous Transfer Mode (ATM), Label Switched Paths in Multi-Protocol Label Switching (MPLS) and optical paths in Wavelength Division Multiplexing (WDM). Examples of Tunnel Signaling include Constrained Routing Label Distribution Protocol (CRLDP) and Resource Reservation Signaling Protocol (RSVP).
Paths for tunnels can be selected with or without human intervention. A network operator may select a path for a tunnel or, given appropriate path selection intelligence, a path for a tunnel may be selected by an edge node or a network management node. The capability of a network to select paths for its tunnels is fundamental to automated network capacity management. Such a path selection capability in known networks is exemplified by the Private Network-Network Interface (PNNI) standard in use in ATM networks or the Open Shortest Path First (OSPF) standard in use in IP networks. Using either PNNI or OSPF, a network node may generate a routing table, comprising a list of paths to each other node in the network, that can be used to select paths for tunnels. Path selection however, is not necessarily limited to those routing tables. It is possible to use the information obtained via OSPF, with regard to network capacity and its utilization, to select paths different than those in the routing tables.
Once a tunnel is created, it may be used by a number of connections. Subsequent to a request for a connection through a network being directed to an appropriate tunnel, the connection is typically examined by Admission Control (AC), which allows only as much traffic as the tunnel can accommodate within its capacity. The AC rejects a connection if the connection would increase the traffic carried by a tunnel beyond the tunnel capacity. AC is currently implemented in ATM networks as Call Admission Control (CAC), but it can be adapted to other types of tunnels.
Work on an adaptive and automated capacity management has so far concentrated on capacity adjustment for virtual paths, resulting in a number of approaches (see H. Saito, “Dynamic Resource Allocation in ATM Networks”, IEEE Communications Magazine, May 1997 and A. S. Maunder, P. S. Min, “Dynamic Bandwidth Allocation to Virtual Paths”, International Conference on Performance, Computing and Communication—IPCCC '98), which use CAC to monitor traffic carried by virtual paths and modify their capacity if it is not adequate. They differ from each other by the assumptions made about the traffic, the traffic models used, the estimators of capacity needs of the traffic and the triggers for modifying virtual path capacity. A further proposal, described in S. Shioda, H. Toyoizumi, H. Yokoi, “Self-Sizing Network: A New Network Concept Based on Autonomous VP Bandwidth Adjustment”, International Tele-traffic Congress ITC 15, Washington, D.C., 1997, includes the calls rejected by the CAC into an estimated capacity requirement for each virtual path. These approaches are, unfortunately, limited to ATM networks.
A different approach is taken in M. Chatzaki, S. Sartzetakis, N. Papadakis, C. Courcoubetis, “Resource Allocation in Multi-service MPLS”, 7th International Workshop on QoS, London, England, 1999. The authors describe a solution for an automated management of capacity in MPLS networks wherein the capacity of each link in the network is divided between different classes of service. By doing this, a virtual network is created for each class of service. Each virtual network has its own routing, which is independent of the routing in the other virtual networks. The routing directs new calls to the least utilized routes. When a virtual link, i.e. a part of a link assigned to a particular class of service, becomes congested, it is given more capacity at the expense of the other virtual links. This approach is also limited to a specific type of network, namely Multi-service MPLS.
Consequently, a need exists for a adaptive and automated capacity management approach applicable to a broad range of network types. Further, the approach should address the dual traffic management problems facing network operators. First, that the revenues of installed network capacity are to be maximized while meeting the QoS needs of unpredictable traffic and second, that the network capacity is to be increased with minimum time and overhead costs.